The present invention is directed to monitoring and analyzing industrial operations and more specifically, to a system and method for monitoring and analyzing industrial operations such that data is captured, copied, and stored for analysis.
In the past machines were self-contained mechanical devices generally having multiple parts that were controlled using various mechanical control systems. As machines developed, electronic control systems have replaced or supplemented the mechanical control systems and now many control systems operate apart from the machine itself utilizing communication systems that operate through one or more communication networks to transfer control commands from a controller, such as a programmable logic control systems (PLC), to the machine. Communication systems typically operate to transfer information, such as data and data packages, for review and storage in various device databases and often include data derived from sensors, controllers and other sources that operate together to monitor the operation of such machines.
As computational capabilities of large computer systems have become faster and cheaper, there is a benefit to being able to analyze industrial systems containing one or more machines or components on a macro scale. While a failure in most industrial operations are not life threatening, a component or system stoppage, such as for example on a packaging line, can result in thousands of dollars of lost production. Accordingly, for industrial systems, it is desirable to do predictive analysis on a single industrial system to predict when similar systems or system components need to receive maintenance or servicing prior to expected failure. Many industrial systems, which often incorporate one or more components operating within a network, would also benefit by having predictive analytics incorporated into to the systems. However, many industrial systems operating in a network have been in continuous operation for many years and operators are hesitant or unwilling to change the PLC programming controlling such systems. Therefore, it would be desirable to be able to monitor and analyze industrial operations comprising one or more industrial components operating within a network without having to reprogram or modify existing control programs.
Communication between the various industrial components within a network also now typically operate at a sub-millisecond level and millions of characters of data per second are exchanged without the need of human interaction. For network developers, it would be desirable to monitor such communication and data being transferred and exchanged between the industrial components operating within a communication network. Various systems have been developed for providing analytics to such industrial networks. One method that has been utilized is to monitor event-based data from human machine interface (HMI) or open platform communications (OPC) tags. When the state of the tag changes, a monitor would reflect that an error occurred. By placing the event-based data into a historian program and analyzing it over time, it is possible to determine what errors were occurring most often. This event and status based system is considered low risk because it only requires reprogramming of the HMI screes and not the reprogramming of the PLC. Unfortunately, such event-based monitoring and predictive systems are limited by using data after an error has occurred and not data generated just prior to and during an error. Other monitoring systems, for example the free and open-source packet analyzer known as WIRESHARK, have been developed for network troubleshooting, analysis, software and communications protocol development, and education. In operation, such systems operate to monitor network communication and to allow users to pick out specific types of data (data packets) and display all the communication traffic addressed to one of the interface's configured addresses. However, the systems are not always sufficient to see all network traffic. Further, the systems do not operate to receive data and place the data in a query-able database necessary for performing detailed analysis, such as a predictive analysis.
Another problem with systems that monitor network communications and obtain data being transferred in a data stream is that such systems operate to interact with the data stream to collect and store data. This interaction often interrupts, disturbs or Interferes with the data stream and with data being transmitted to and from the various industrial components and to and from the master controller. Further, such systems do not operate to convert data, such as Common Industrial Protocol data, being transmitted through a communications network into a query-able format and stored in a query-able database. Often such systems operate using an “update-in-place” methodology in which a performer consumes the data as it detects the data. The performer captures data and updates the data stored in the database by replacing (overriding) the existing stored data. Accordingly, since the data stored in the database is constantly being replaced with new current data, such systems are unable to perform analysis using past or historical data in order to make a predictive analysis.
Until now, in order to monitor industrial systems and obtain and store data for predictive analysis, the PLC programming controlling the industrial system would require modifying or creating and/or installing new programming. This would often require upgrading the PLC hardware as well as installing new programming. The upgraded PLC hardware and/or programming adds new and potentially instable variables that can result in significant loss of time and Increase in costs due to system failure or interruption. Thus, for many operations the potential lost time and increase in costs makes such changes in the PLC programming and/or PLC hardware unacceptable.